Continuous-Time Neural Networks Can Stably Memorize Random Spike Trains

Abstract

The paper explores the capability of continuous-time recurrent neural networks to store and recall precisely timed spike patterns. We show (by numerical experiments) that this is indeed possible: within some range of parameters, any random score of spike trains (for all neurons in the network) can be robustly memorized and autonomously reproduced with stable accurate relative timing of all spikes, with probability close to one. We also demonstrate associative recall under noisy conditions. In these experiments, the required synaptic weights are computed offline, to satisfy a template that encourages temporal stability.